人才振兴背景下大学生志愿服务乡村缘由和影响因素分析

志愿服务乡村是社会参与人才振兴战略的重要方式,大学生在其中承担着重要角色。“乡愁文化”和“社会理性”是大学生志愿服务乡村缘由所在;性别、年级、学校类型、了解程度和志愿组织对大学生志愿服务乡村有显著影响。大学生志愿服务的着力点在于智力支持且具有文教活动倾向性,但也存在意愿与行动脱节、认知流于表面、能动性不强、缺乏思想引领以及对小微志愿组织的支持缺位等问题。乡愁文化嵌入大学文化、引入激励机制、打造志愿组织“大家庭”、牢守宣传育人“主阵地”、强化智力支持优势、补齐理论技能短板、关爱“小微组织”成长、构建“三位一体”支撑体系等可有效优化大学生志愿服务乡村。     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

The value of being precise

This study investigates how individuals assess imprecise information. We focus on two essential dimensions of decision under uncertainty, outcomes and probabilities, and their respective precision. We believe the precision of information is highly relevant in the investment setting, as reflected in the well-known “home (familiarity) bias”, and the outcome and probability dimensions, separately or jointly, may affect investors’ knowledge of uncertainty and perceived risk of the investment options, and subsequently affect investors’ choices. To test this conjecture, we conducted three experiments. Our results show that 1) participants demonstrate a pattern of preference for precision and aversion of extreme vagueness and associate vagueness with higher perceived risk and lower investment (experiments one and two); 2) participants prefer vague outcome information to vague probability information (experiment two); 3) familiarity indeed positively affects the precision of estimated values, but this association is stronger for the outcome dimension than for probabilities (experiment three). Our results confirm that precision in information, especially in the outcome dimension has an impact on investors’ resource allocation choices.     

Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

Visualization of generalized mean estimators using auxiliary information in survey sampling

Abstract

The mean estimators with ratio depend on multiple auxiliary variables and unknown parameters in a finite population setting. We propose a new generalized approach with matrices for modeling the mutivariate mean estimators with two auxiliary variables. Our approach brings naturally a graphical analysis for comparing mean estimators.     

对区域高等教育战略规划的若干思考--基于《加利福尼亚州高等教育总体规划（1960-1975）》的分析

《加利福尼亚州高等教育总体规划（1960—1975）》是区域高等教育战略规划领域的成功范例，规划内容通过州宪法性效力、州法律法规性效力以及行业约定性效力三种形式发挥作用。准确把握关键问题、秉持院校自治理念、深入开展调研等做法值得借鉴。我国区域高等教育机遇与挑战并存，教育规划应当坚持治理理念、以人为本理念和系统论理念，对区域高校定位作出合理预期与安排，引导区域高等教育健康和谐发展。     

市场分割、政企关系与异地市场拓展

中国的市场仍旧存在某种程度的分割，如何突破这种市场分割到异地拓展市场是企业要考虑的现实问题。以前的研究主要是从省级层面或国家层面进行宏观分析。文章创新地从政企关系视角入手来考察市场分割情形下微观层面的企业异地市场拓展，也是首先把市场分割（和异地市场）分为省内（本市外）市场、省外市场和海外市场三个不同层面来研究的论文。用世界银行的调查数据从微观企业个体层面检验了企业异地市场拓展的可能。实证研究结果发现，市场分割确实阻碍了企业异地市场拓展，但这仅限于省内市场而不是省外市场。此外，市场分割是有方向性的，从地方保护主义严重的市场里走出来的企业更容易拓展异地市场，但此结论仅适用于国内市场拓展而不适用于海外市场拓展。良好的政企关系是有利于企业异地市场拓展的，但仅限于省外市场，对省内市场以及海外市场是无效的。文章解释了其中的原因，并分析了对于企业实践的启示。     

虚拟空间交织的取向原则

虚拟空间交织是人们常用的思维与认知方式之一。本文通过对交织及其原则的介绍,描述了虚拟空间交织的运作,指出虚拟空间交织构建中主观取向的原则应为最佳关联。     

论非文科专业人文社会科学教学改革的必然趋势

非文科专业人文社会科学教学改革是时代的呼唤和社会可持续发展的现实要求 ,是 2 1世纪高等教育发展的历史趋势 ,是我国社会主义现代化建设事业的客观需要。